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Progressive Tool Discovery

Example agent demonstrating dynamic tool discovery for SGR Agent Core.

Problem

When using multiple MCP servers (Jira, Confluence, GitHub, GDrive), each adds dozens of tools. With ~60 tools the LLM context becomes bloated — local models can't handle it, and paid APIs waste tokens on irrelevant tool descriptions.

Solution

The agent starts with a minimal set of system tools (reasoning, planning, clarification, final answer) and dynamically discovers additional tools via SearchToolsTool.

User query → Agent reasons → needs web search → calls SearchToolsTool("search the web")
→ WebSearchTool discovered and added to active toolkit → Agent uses WebSearchTool

How it works

  1. Init: Toolkit is split into system tools (subclasses of SystemBaseTool) and discoverable tools
  2. Runtime: Only system tools + already discovered tools are sent to LLM
  3. Discovery: Agent calls SearchToolsTool with a natural language query
  4. Matching: ToolFilterService uses BM25 ranking + regex keyword overlap to find relevant tools
  5. Activation: Matched tools are added to the active toolkit for subsequent calls

Key components

Component Description
ProgressiveDiscoveryAgent Agent subclass that manages system/discovered tool split
SearchToolsTool Meta-tool for discovering new tools by capability description
ToolFilterService Stateless BM25 + regex matching service

Usage

cp config.yaml.example config.yaml
# Edit config.yaml with your API key and MCP servers
sgr --config-file config.yaml

Architecture

ProgressiveDiscoveryAgent
├── self.toolkit = [ReasoningTool, SearchToolsTool, ...]  (system tools)
├── context.all_tools = [WebSearchTool, ...]  (discoverable)
└── context.discovered_tools = []  (accumulates at runtime)

context is a ProgressiveDiscoveryContext(AgentContext) — extends the base context with discovery-specific fields.

_get_active_tools() returns system_tools + discovered_tools — used by both _prepare_tools() and _prepare_context().